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Mutations and fitness tradeoffs in bacteria

If you've ever trained for a track event, you know there are two ways to run. Training for a long distance running event means you have to run economically – wasted movement costs valuable energy. Sprinting, on the other hand, focuses on powerful movements made with intense effort.

Top runners should ideally be good at both. For example, at the end of a long distance event, runners typically become more concerned about crossing the finish line as soon as possible – sprint-like movements would help here. But long distance runners typically do not change their form – they lengthen their stride as they near the finish line. They cannot run like trained sprinters because they have invested heavily in making energetically economic movements.

In evolutionary biology and ecology, such tradeoffs are a recurring theme. They are pervasive across diverse forms of life and shape diversity in biological traits, both within a species and between different species. A new study from Deepa Agashe's group at NCBS, Bangalore has measured the occurrence and fitness impact of tradeoffs caused by single mutations in bacterial genomes.Despite the ubiquity of tradeoffs, the mechanisms through which they arise are not very well understood. One of the important mechanisms that can cause tradeoffs is antagonistic pleiotropy, i.e. when single mutations in an organism's genome make it better at one function but worse at another. If such mutations are frequent and cause large changes in the organism’s performance, then they could explain why we often see tradeoffs. As evolution proceeds, they would result in divergence of traits that affect an organism's survival and reproduction.

Despite the common perception that antagonistic pleiotropy causes tradeoffs, measurements of the actual incidence and impact of antagonistic pleiotropy have been rare. The few studies which have studied antagonistic pleiotropy only analysed mutations that are beneficial to the organism in a specific habitat. This is similar to a situation where a researcher uses a sample of only the best runners in the country to ask if sprinting speed trades off with endurance. It is useful to sample all sorts of runners because if the nature of the racetrack changes, you may end up with a very different set of top runners. For example, the runners that do best in Olympic track events may not be the runners that shine on a mud road.

Mrudula Sane and Joshua Miranda from Deepa Agashe's group at NCBS measured both the incidence and impact of a set of randomly sampled non-lethal mutations, including beneficial, neutral and deleterious mutations. They did this by allowing multiple populations of a bacterium, Escherichia coli, to evolve in the absence of natural selection for hundreds of generations. Then they sequenced the entire genomes of some bacteria to identify those that contained only a single mutation. To measure the incidence of antagonistic pleiotropy, they tested the performance of these populations by measuring how quickly these populations grew on a range of different sugars. Although previous studies had demonstrated clear tradeoffs in the use of these sugars, the cause of the tradeoffs had remained unclear.

The researchers found that very few mutations showed antagonistic pleiotropy, but it was initially difficult to say whether this was really surprising. "Because there is no theoretical prediction about the incidence of antagonistic pleiotropy, we had no framework to evaluate our results", says Deepa Agashe. After talking to some colleagues at a conference, Mrudula and Deepa decided to build their own null expectation. They used a simulation-based framework to estimate the incidence of antagonistic pleiotropy one might expect by chance alone, given the observed impact of a mutation on growth in each sugar. Compared to this null estimate, they found that antagonistic pleiotropy is quite rare. This means that very few tradeoffs can be explained by antagonistically pleiotropic mutations.Further analysis revealed that of all the mutations that affected bacterial growth, very few had a large impact. It was this small set of important large-effect mutations that are more likely to show antagonistic pleiotropy across environments. Unexpectedly, the group also found that the mutations that made the bacterium a “top runner” while eating sugar A caused only a small decrease in growth on sugar B. In other words, bacteria that train to run long-distance do not pay a very large cost in sprinting ability.

The new work is important because it is the largest study thus far to quantify the incidence of antagonistic pleiotropy, and it focuses on a broad range of mutations with diverse functional effects in bacteria. What do the results mean in the context of the common belief that antagonistic pleiotropy frequently causes tradeoffs? Agashe suggests that we have perhaps oversimplified and over relied on antagonistic pleiotropy as a mechanism to explain tradeoffs. Instead, Sane adds, “It's possible that the abundant tradeoffs that are seen in nature are due to accumulation of multiple mutations, or due to selection for specific traits”. This process can take several thousands of generations, many times longer than if tradeoffs occurred through a single mutation. As those who have trained for marathons know, quick fixes are rare and difficult to come by.

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National Centre for Biological Sciences (NCBS), Tata Institute of Fundamental Research (TIFR) is a premier centre for research and teaching in frontier areas of biology in India. Our research at NCBS covers a diverse set of subjects in frontier areas of modern biology ranging in scope from atomic to population level studies.